Method and system of a target result optimizing application

ABSTRACT

A system of optimizing a target result receives, by a client input server, a target result from a user, where the target result comprises at least one of a goal, and a statistical probability that the target result is achievable, and where the target result is to be achieved during a time period. The system compiles, by an output server, an interactive strategy comprising a timeline to achieve the target result. The system optimizes at least a portion of the interactive strategy by modeling at least one future performance model associated with the target result, and determining an optimal strategy for the target result comprising a target result value. The system renders the optimized interactive strategy, the statistical probability, and the target result for the user on a real-time interactive display, where the statistical probability is predictive of achieving the target results.

CROSS-REFERENCE TO RELATED APPLICATIONS

This patent document is a continuation of U.S. patent application Ser.No. 15/591,162, filed on May 10, 2017, which claims the benefit ofpriority to U.S. Provisional Patent Application No. 62/382,068, filedAug. 31, 2016, the entire contents of which are incorporated herein byreference.

BACKGROUND

When striving to achieve a target result over the course of a timeperiod, such as an investment goal over an investment period, it isoften necessary to determine the amount of assets to devote toward thetarget result, and how to allocate those assets over the course of thetime period. Therefore, it would be helpful to have a way toautomatically optimize the allocation of assets over the course of thetime period to attain the highest probability of achieving the targetresult.

SUMMARY

According to one embodiment disclosed herein, in a method for optimizinga target result, a client input server receives a target result from auser via a real-time interactive display. The target result comprises atleast one of a goal, and a statistical probability that the targetresult is achievable. The target result is to be achieved during a timeperiod beginning at a target result start point, and ending at a targetresult end point. The target result is transmitted from the real-timeinteractive display to the client input server. An output servercompiles an interactive strategy to achieve the target result, where theinteractive strategy comprises a timeline starting at the target resultstart point, and ending at the target result end point. The client inputserver transmits the target result to the output server. An optimizerapplication with an optimizer interface that interfaces with at leastone of a client input interface of the client input server and an outputinterface of the output server optimizes at least a portion of theinteractive strategy. The portion of the interactive strategy isoptimized by i) modeling, by a model generator, at least one futureperformance model associated with the target result, where the optimizerapplication obtains the future performance model from the modelgenerator, and ii) determining an optimal strategy for the target resultat the target result end point, where the optimal strategy comprises atarget result value. The method renders the optimized interactivestrategy, the statistical probability, and the target result for theuser on the real-time interactive display, where the statisticalprobability is predictive of achieving the target results. The optimizedinteractive strategy is transmitted from the output server to the clientinput server to be rendered on the real-time interactive display. Afterdetermining the optimal strategy, the method determines at least onesecond optimal strategy for the target result at a first location in thetimeline between the target result end point and the target result startpoint, using the future performance model. The future performance modelprovides a performance indicator for the target result at the firstlocation to achieve a sub target result value at the target result endpoint.

In one aspect of embodiments disclosed herein, the method receives aninvocation from the user, via the real-time interactive display, toactivate the optimized interactive strategy. The method simulates theoptimized interactive strategy over the course of the time period, onthe real-time interactive display, where the simulated optimizedinteractive strategy renders at least one second optimal strategy andthe optimal strategy, starting at the target result start point andending at the target result end point.

In one aspect of embodiments disclosed herein, when the method simulatesthe optimized interactive strategy over the course of the time period,the method determines a plurality of optimized interactive strategies,and randomly selects one of the plurality of optimized interactivestrategies to present to the user on the real-time interactive display.

In one aspect of embodiments disclosed herein, when the method simulatesthe optimized interactive strategy over the course of the time period,the method incorporates data streamed from a real time online databaseinto at least one of the simulated optimized interactive strategy and afuture simulated optimized interactive strategy.

In one aspect of embodiments disclosed herein, when the method simulatesthe optimized interactive strategy over the course of the time period,the method determines, during the rendering, that the target result isnot achievable. The method then iteratively adjusts at least one of aplurality of inputs associated with the target result, and simulates theoptimized interactive strategy until the target result is achieved.

In one aspect of embodiments disclosed herein, when the method simulatesthe optimized interactive strategy over the course of the time period,the method determines, during the rendering, that the target result isachievable. The method then iteratively adjusts at least one of aplurality of inputs associated with the target result and simulates theoptimized interactive strategy until the target result is within anacceptable target result range.

In one aspect of embodiments disclosed herein, when the method simulatesthe optimized interactive strategy over the course of the time period,the method determines, during the simulating, that the target result isnot achievable. The method then determines a highest suboptimal targetresult that is achievable and a highest statistical probability that thehighest suboptimal target result is achievable. The method automaticallymodifies at least one of a plurality of inputs and/or prompts the userto change at least one of the plurality of inputs. The method thenre-simulates the optimized interactive strategy over the course of thetime period.

In one aspect of embodiments disclosed herein, the method provides theuser with at least one interactive control to interact with thesimulated optimized interactive strategy during the simulation, via thereal-time interactive display. The method obtains at least one subtarget result from the user during the simulation of the optimizedinteractive strategy. The method renders the simulated optimizedinteractive strategy with at least one sub target result incorporatedinto the optimized interactive strategy. The method obtains at least onesub target result from the user via the real-time interactive display,where at least one sub target result occurs between the target resultstart point and the target result end point. The method incorporates atleast one sub target result into the optimized interactive strategy. Thesub target result comprises adding a first asset and/or removing asecond asset.

In one aspect of embodiments disclosed herein, when the methodincorporates at least one sub target result into the optimized strategy,the method provides the user with at least one interactive control toincorporate at least one sub target result into the simulated optimizedinteractive strategy.

In one aspect of embodiments disclosed herein, when the client inputserver receives the target result from the user via the real-timeinteractive display, the method automatically interfaces with an onlineaccount associated with the user to transmit, from the online account tothe client input server, input relevant to the target result. Theautomatic interfacing comprises automatically logging into the onlineaccount.

In one aspect of embodiments disclosed herein, when the client inputserver receives the target result from the user via the real-timeinteractive display, the method receives the goal from the user. Whenthe optimizer application optimizes at least a portion of theinteractive strategy, the method determines the statistical probabilitybased on the goal inputted by the user.

In one aspect of embodiments disclosed herein, when the client inputserver receives the target result from the user via the real-timeinteractive display, the method receives the statistical probabilityfrom the user. When the optimizer application optimizes at least aportion of the interactive strategy, the method determines the goalbased on the statistical probability inputted by the user.

In one aspect of embodiments disclosed herein, when the optimizerapplication optimizes at least a portion of the interactive strategy,the method automatically modifies at least one of a plurality of inputs,and/or prompts the user to change at least one of the plurality ofinputs. The method then re-optimizes at least a portion of theinteractive strategy. The plurality of inputs comprise at least one ofan investment period, an annuity drawing period, an initial investment,an investment maximum, a target annuity, and/or a periodic deposit.

In one aspect of embodiments disclosed herein, the model generatormodels the future performance model comprising resources used to achievethe target result, and an allocation of each of the resources in thefuture performance model.

In one aspect of embodiments disclosed herein, when the model generatormodels the future performance model comprising resources used to achievethe target result, and an allocation of each of the resources, the modelgenerator determines the allocation of each of the resources at anypoint during the time period to attain a highest probability ofachieving the target result. The method then provides a recommendationto redistribute the allocation of each of the resources to achieve thetarget result.

In one aspect of embodiments disclosed herein, the model generatormodels the future performance model using data streamed from a real timeonline database.

In one aspect of embodiments disclosed herein, when the model generatormodels the future performance model, the method calculates a probabilitydistribution function for the target result at the target result endpoint.

In one aspect of embodiments disclosed herein, when the methodcalculates a probability distribution function for the target result atthe target result end point, the method selects the distributionfunction target result value from at least two distribution functionsbased on a risk profile specified by the user in the real-timeinteractive display.

In one aspect of embodiments disclosed herein, the model generatormodels the future performance model using an example of a pastperformance.

In one aspect of embodiments disclosed herein, when the methoddetermines at least one second optimal strategy, the method iterativelydetermines at least one third optimal strategy for the target result, ata plurality of intervals in the timeline between the first location inthe timeline and the target result start point, using the futureperformance model. The future performance model provides a performanceindicator, at each of the plurality of intervals, to achieve the targetresult value at the target result end point.

In one aspect of embodiments disclosed herein, when the methoditeratively determines at least one third optimal strategy for thetarget result, the method utilizes a Monte Carlo simulation to calculatethe target result value at the target result end point.

In one aspect of embodiments disclosed herein, when the methoditeratively determines at least one third optimal strategy for thetarget results, the method incorporates a previous interval targetresult value when determining the third optimal strategy for the targetresult at a current interval. The previous interval target result valueis an estimated target result value calculated at a previous interval,and the current interval is closer to the target result start point thanthe previous interval.

In one aspect of embodiments disclosed herein, when the method rendersthe optimized interactive strategy, the method renders the optimizedinteractive strategy real-time as the optimized interactive strategy isgenerated, and/or renders a previously generated optimized interactivestrategy that is displayed when a user invokes the rendering of theoptimized interactive strategy.

In one aspect of embodiments disclosed herein, when the method rendersthe optimized interactive strategy, the method provides the user with arecommendation to achieve the target result.

System and computer program products corresponding to theabove-summarized methods are also described and claimed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features and advantages will beapparent from the following description of particular embodiments of theinvention, as illustrated in the accompanying drawings in which likereference characters refer to the same parts throughout the differentviews. The drawings are not necessarily to scale, emphasis instead beingplaced upon illustrating the principles of various embodiments of theinvention.

Various aspects of at least one embodiment are discussed below withreference to the accompanying figures, which are not necessarily drawnto scale, emphasis instead being placed upon illustrating the principlesdisclosed herein. The figures are included to provide an illustrationand a further understanding of the various aspects and embodiments, andare incorporated in and constitute a part of this specification, but arenot intended as a definition of the limits of any particular embodiment.The figures, together with the remainder of the specification, serve toexplain principles and operations of the described and claimed aspectsand embodiments. In the figures, each identical or nearly identicalcomponent that is illustrated in various figures is represented by alike numeral. For purposes of clarity, not every component may belabeled in every figure.

FIG. 1 illustrates a system for optimizing a target result according toembodiments disclosed herein.

FIG. 2 illustrates an example user input screen for a target resultoptimizing application, according to embodiments disclosed herein.

FIG. 3 illustrates an example asset value table created by the targetresult optimizing application, according to embodiments disclosedherein.

FIG. 4 illustrates an example interactive simulation input screen in thetarget result optimizing application, according to embodiments disclosedherein.

FIG. 5 illustrates an example interactive simulation input screen beforea user begins an automated simulation run in the target resultoptimizing application, according to embodiments disclosed herein.

FIG. 6 illustrates an example interactive simulation input screen when auser begins an automated simulation run in the target result optimizingapplication, according to embodiments disclosed herein.

FIG. 7 illustrates a continuation of the example automated simulationrun of FIG. 6, according to embodiments disclosed herein.

FIG. 8 illustrates a continuation of the example automated simulationrun of FIG. 7, according to embodiments disclosed herein.

FIG. 9 is an example forecast calculated by the target result optimizingapplication, according to embodiments disclosed herein.

FIG. 10 is an example trading activity table created by the targetresult optimizing application, according to embodiments disclosedherein.

FIG. 11 illustrates a high level system for optimizing a target resultaccording to embodiments disclosed herein.

DETAILED DESCRIPTION

Embodiments disclosed herein provide optimizing a target result. Thefollowing description is presented to enable one of ordinary skill inthe art to make and use embodiments disclosed herein, and are providedin the context of a patent application and its requirements. Variousmodifications to the embodiment will be readily apparent to thoseskilled in the art and the generic principles herein may be applied toother embodiments. Thus, embodiments disclosed herein are not intendedto be limited to the embodiment shown but is to be accorded the widestscope consistent with the principles and features described herein.

Embodiments disclosed herein can take the form of an entirely hardwareembodiment, an entirely software embodiment or an embodiment containingboth hardware and software elements. In a preferred embodiment,embodiments disclosed herein are implemented in software, which includesbut is not limited to firmware, resident software, microcode, etc.

Furthermore, embodiments disclosed herein can take the form of acomputer program product accessible from a computer-usable orcomputer-readable medium providing program code for use by or inconnection with a computer or any instruction execution system. For thepurposes of this description, a computer-usable or computer readablemedium can be any apparatus that can contain, store, communicate,propagate, or transport the program for use by or in connection with theinstruction execution system, apparatus, or device.

The medium can be an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system (or apparatus or device) or apropagation medium. Examples of a computer-readable medium include asemiconductor or solid state memory, magnetic tape, a removable computerdiskette, a random access memory (RAM), a read-only memory (ROM), arigid magnetic disk and an optical disk. Current examples of opticaldisks include compact disk-read only memory (CD-ROM), compactdisk-read/write (CD-R/W) and DVD.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code in order to reduce the number of times code must beretrieved from bulk storage during execution.

Input/output or I/O devices (including but not limited to keyboards,displays, point devices, etc.) can be coupled to the system eitherdirectly or through intervening I/O controllers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments disclosed herein. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified local function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of embodimentsdisclosed herein. As used herein, the singular forms “a”, “an” and “the”are intended to include the plural forms as well, unless the contextclearly indicates otherwise. It will be further understood that theterms “comprises” and/or “comprising,” when used in this specification,specify the presence of stated features, integers, steps, operations,elements, and/or components, but do not preclude the presence oraddition of one or more other features, integers, steps, operations,elements, components, and/or groups thereof. The examples of the methodsand systems discussed herein are not limited in application to thedetails of construction and the arrangement of components set forth inthe following description or illustrated in the accompanying drawings.The methods and systems are capable of implementation in otherembodiments and of being practiced or of being carried out in variousways. Examples of specific implementations are provided herein forillustrative purposes only and are not intended to be limiting. Also,the phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting. Any references toexamples, embodiments, components, elements or acts of the systems andmethods herein referred to in the singular may also embrace embodimentsincluding a plurality, and any references in plural to any embodiment,component, element or act herein may also embrace embodiments includingonly a singularity. References in the singular or plural form are notintended to limit the presently disclosed systems or methods, theircomponents, acts, or elements. The use herein of “including,”“comprising,” “having,” “containing,” “involving,” and variationsthereof is meant to encompass the items listed thereafter andequivalents thereof as well as additional items. References to “or” maybe construed as inclusive so that any terms described using “or” mayindicate any of a single, more than one, and all of the described terms.

FIG. 1 illustrates a system for optimizing a target result according toembodiments disclosed herein. The computer system 100 is operationallycoupled to a processor or processing units 106, a memory 101, and a bus109 that couples various system components, including the memory 101 tothe processor 106. The bus 109 represents one or more of any of severaltypes of bus structure, including a memory bus or with one or moreexternal devices 111, such as a display 110, via I/O interfaces 107. Thememory 101 may include computer readable media in the form of volatilememory, such as random access memory (RAM) 102 or cache memory 103, ornon-volatile storage media 104. The memory 101 may include at least oneprogram product having a set of at least one program code modules 105that are configured to carry out the functions of embodiments of thepresent invention when executed by the processor 106. The computersystem 100 may communicate with one or more networks via network adapter108.

In a target result optimizing application operating on the processor106, a client input server 210, as illustrated in FIG. 11, receives atarget result from a user via a real-time interactive display 200 asillustrated in FIG. 2. The target result comprises at least one of agoal, and a statistical probability that the target result isachievable. The target result may be, for example, an investment planfor a pension. The goal may be, for example, a pension with an annualwithdrawal amount (also referred to as a withdrawal plan), and thestatistical probability that the target result is achievable may be, forexample, a probability percentage that the investment plan isachievable. The target result is to be achieved during a time periodsuch as an investment period, beginning at a target result start point,and ending at a target result end point, such as the beginning of aninvestment period, and the end of the investment period.

A user enters input in the interactive input screen that may be onlineor a desktop application. The target result optimizing applicationaccepts input related to the target result. For example, the targetresult may be related to an investment portfolio. In this scenario, theinput may include but is not limited to the type of currency to beprofiled (i.e., USD, Euro, Pesos, Lira, etc.), the age at which the userstarts making the investment, the age at which the user stops making theinvestment, the age at which the user desires to begin to draw from theinvestment, an annual deposit, a minimum and/or maximum annual deposit,an initial investment, stop time asset value (i.e., how much money isleft in the fund at the end of the investment period, also referred toas the target result value), stop percentile (i.e., the lowest successrate), bond percentage at the start of the investment period, bondpercentage at the end of the investment period, desired annuity amount,stock index, bond index, inflation rate (the stock index, bond index,and/or inflation rate may be automatically obtained or manually enteredby the user), transaction cost of each buy/sell trade. The user mayspecify a probability percentage that the withdrawal plan specified willbe achieved, for example, “I aim for an 80% chance of reaching mywithdrawal plan”. The user may choose whether to optimize a strategyassociated with the target result. The user may also choose whetherinputs and outputs are adjusted for inflation.

In an example embodiment, the target result may be an investment plan,and the strategy may be an investment strategy. Within the investmentstrategy, the percentage of financial instruments within the financialportfolio, such as stocks and bonds, may be tailored at each point ofthe duration of the investment period. For example, the user who isinvesting at 25 years of age may split the stocks and bonds according toa higher risk ratio than the user who is investing at 65 years of age.The target result optimizing application adjusts the percentage ofstocks and bonds according to a timeline associated with the duration ofthe investment (i.e., the investment period or the time period). In thisexample, the investment includes stocks and bonds, but the investmentmay include any assets.

After the client input server 210 receives the target result from a uservia a real-time interactive display 200, the target result istransmitted from the real-time interactive display 200 to the clientinput server 210. The client input server 210 transmits the targetresult to an output server 220. The output server 220 compiles aninteractive strategy to achieve the target result. The interactivestrategy comprises a timeline starting at the target result start point,and ending at the target result end point. For example, the targetresult may be an investment plan, where the target result start pointand the target result end point are, respectively, the beginning and theend of the investment period. In this scenario, the withdrawal plan(also referred to as the goal) may comprise any payments specified bythe user and the point(s) along the investment period when the userdesires to withdraw those payments. In other words, the withdrawal planmay be a pension with an annual withdrawal amount. The withdrawal planallows the user to also include withdrawals during various points alongthe investment period. For example, the user may want to make a lump sumwithdrawal at some point during the investment period, or may want tomake periodic withdrawals during the investment period. Thus, thewithdrawal plan may include, but is not limited to, a pensionwithdrawal, lump sum withdrawal, any periodic payment, or any paymentaccording to any schedule. In other words, the withdrawal plan may bewithdrawn yearly, monthly, etc., or may be withdrawn according to anyschedule, periodic or otherwise, or a one-time withdrawal.

The target result optimizing application provides a user with a strategythat matches the user's goal. In an example embodiment where the targetresults is an investment plan, the strategy may be an investmentstrategy that matches the user desired withdrawal plan (i.e., the goal).In this scenario, the investment portfolio comprises a plurality offinancial instruments. The target result optimizing application acceptsa variety of inputs from the user, as shown in FIG. 2. The investmentperiod may include the time when the user is contributing to theinvestment portfolio, and the time when the user is drawing from theinvestment portfolio (for example, the user may start investing at age25, and may start drawing from the investment at age 65). The user mayset an annual investment maximum, or the target result optimizingapplication may be programmed according to existing financialregulations for investment maximums. The target result optimizingapplication calculates the amount of investments needed over the (userspecified) investment period to achieve the specified withdrawal planwith the specified probability.

In an example embodiment, the target result optimizing applicationdetermines an optimal strategy for the target result at the targetresult end point. An investment strategy is an example optimal strategyfor the target result of an investment plan. The user may specify thenumber of years during which they wish to invest in the financialinstrument(s), the initial investment, the periodic deposits, the yearsduring which they wish to draw from the financial portfolio, and thetarget annuity (that they wish to receive during the years they aredrawing from the financial portfolio). For example, the user may specifythat they are aiming for an 80% chance of reaching their withdrawalplan. Based on this input, the target result optimizing applicationautomatically calculates an optimal financial strategy for thatinvestment portfolio (given a model used to calculate that investmentportfolio), aiming for the success rate specified by the user. Forexample, the target result optimizing application may specify, “If youfollow this plan, there is an 80% chance that your monthly annuity willbe at least $5500” (an annuity amount entered by the user in thereal-time interactive display 200). The target result optimizingapplication may also suggest how increasing the periodic deposit affectsthe probability that the withdrawal plan will be achieved. For example,the target result optimizing application may specify, “For an additional$500 per month, you will have a 90% chance to reach the goal for yourmonthly annuity”.

Continuing with the example of an investment plan as the target result,in an example embodiment, the user may define the withdrawal plan (i.e.,the goal), or the user may request that the target result optimizingapplication calculates the withdrawal plan on behalf of the user. Forexample, the user may know when he/she anticipates drawing a pension,and when the user plans on retiring, and therefore can define thedetails of the withdrawal plan. Alternatively, the user may want to knowwhen he/she can begin to withdraw a pension, or how much of a pensionthe user can expect, given the financial portfolio. Thus, when the userenters input into the interactive input screen, the user may leave somefields blank (such as the initial amount invested, the annualcontribution, the withdrawal amount, or the withdrawal age) and thetarget result optimizing application calculates a withdrawal plan or aprobability percentage for the user. The user may also access apre-defined withdrawal plan that provides the user with a samplewithdrawal plan. The pre-defined withdrawal plan may be helpful if theuser is unsure where to begin with his/her financial investments. In anexample embodiment, the withdrawal plan may be a pension plan. In thisscenario, the withdrawal amount is a pension amount, and the withdrawalage is a pension age as illustrated in FIG. 2 and FIG. 4.

Regardless of whether the user defines some or all of the withdrawalplan (i.e., the goal), or accesses a pre-defined withdrawal plan, thetarget result optimizing application may optimize the strategy (in thiscase an investment strategy) for the goal (i.e., the withdrawal plan).In an example embodiment, after the user provides the input parameters,the client input server 210 connects to an output server 220 andprovides the input parameters. The output server 220 interfaces with anoptimizer application 230, and provides the optimizer application 230with parameters based on the input parameters from the user.

An optimizer application 230 with an optimizer interface that interfaceswith the client input interface (i.e., the real-time interactive display200) of the client input server 210 and/or the output interface of theoutput server 220 optimizes at least a portion of the interactivestrategy as illustrated in FIGS. 3-10, depicting an example investmentplan as the target result. The optimizer application 230 interfaces witha model generator that models at least one future performance modelassociated with the target result, and obtains the future performancemodel from the model generator. The optimizer application 230 determinesan optimal strategy for the target result at the target result endpoint, where the optimal strategy comprises a target result value, forexample, a total amount of money saved for retirement. In this scenario,the optimizer application 230 determines an optimal investment plan atany point during the investment period so as to accumulate the desiredamount of funds at the end of the investment period that will providethe desired amount of pension funds throughout the withdrawal period ofthe pension. The optimizer application 230 determines the allocation forthe financial instruments in the financial portfolio for each yearduring the investment period. The optimizer application 230 determineshow to invest the assets in the financial portfolio, predicting the bestsplits for the financial instruments so as to attain the highestprobability of achieving the withdrawal plan. When making thisdetermination, the optimizer application 230 takes into account, at anygiven point in time, the asset value of the financial portfolio and theyears remaining in the investment period. The target result optimizingapplication uses the information provided by the optimizer application230 to recommend redistributing the allocation of financial instrumentsto achieve the desired annuity. In an example embodiment, the targetresult optimizing application may recommend that assets (such as thefinancial portfolio) in the target result be reallocated. For example,the target result optimizing application may recommend that thepercentages of stocks versus bonds be reallocated, and may advise tosell stocks to purchase bonds, etc. In an example embodiment, thecalculated reallocation of financial instruments may be automaticallyentered as input into financial accounts (for example, the user'sretirement account at a financial institution) to provide constant, upto date, investment strategy advice. In another example embodiment, thetarget result optimizing application may automatically invest in indexfunds, or other simple, low cost, well performing equity funds. Thecomputed allocation may be recalculated at any time the input parameterschange.

The optimizer application 230 determines an optimal strategy for thetarget result at the target result end point, where the optimal strategycomprises a target result value. Using the example of an investment planas a target result, the optimizer application 230 determines theinvestment strategy of how to allocate the financial instruments withinthe financial portfolio for each year during the investment period,where the target result value may be, for example, the total amount ofmoney saved for retirement. To determine this allocation, the optimizerapplication 230 builds an investment strategy table with the number ofyears remaining (within the investment period) on the X axis, and thetotal asset value (at that time) on the Y axis. In an exampleembodiment, the target result optimizing application keeps entries for adiscreet set of asset values, and then uses interpolation when readingthe investment strategy table. Each table cell contains the optimalpercentage of financial instruments to use, and also the probabilitydistribution function for the asset value at the end of the investmentperiod. In an example embodiment, the optimization may be performed forportions of the investment period.

In an example embodiment, after determining the optimal strategy, thetarget result optimizing application determines at least one secondoptimal strategy for the target result at a first location in thetimeline between the target result end point and the target result startpoint, using the future performance model. The future performance modelprovides a performance indicator for the target result at the firstlocation to achieve a sub target result value at the target result endpoint. In another example embodiment, the target result optimizingapplication incorporates a previous interval target result value whendetermining at least one third optimal strategy for the target result ata current interval, where the previous interval target result value isan estimated target result value calculated at a previous interval, andwhere the current interval is closer to the target result start pointthan the previous interval. In other words, the target result optimizingapplication calculates the target result at the end of the time period(first location), calculates the target result at some point prior tothe end of the time period (second location), such as one year prior tothe end of the time period, and then calculates the target result at athird location at some point prior to the second location such that thetarget result optimizing application is calculating the target resultsbackwards, starting at the end of the time period. When the targetresult optimizing application calculates the target result at the thirdlocation, the target result optimizing application incorporates thetarget result calculated at the second location.

The target result optimizing application renders the optimizedinteractive strategy, the statistical probability, and the target resultfor the user on the real-time interactive display 200 as illustrated inFIG. 4 and FIG. 10. The statistical probability is predictive ofachieving the target results. The optimized interactive strategy istransmitted from the output server 220 to the client input server 210 tobe rendered on the real-time interactive display 200. The target resultoptimizing application may render the optimized interactive strategyreal-time as the optimized interactive strategy is generated, and/or mayrender a previously generated optimized interactive strategy that isdisplayed when a user invokes the rendering of the optimized interactivestrategy. The target result optimizing application may also provide theuser with a recommendation to achieve the target result.

Using the example of an investment plan as the target result, the targetresult optimizing application allows a user to specify an expected (ordesired) withdrawal plan, or goal (such as a pension amount that theuser would like to draw upon in retirement), for example, “I would likea $5400 per month pension to draw upon in 35 years from now” asillustrated in FIG. 2 and FIG. 4. The target result optimizingapplication returns the probability percentage that the expectedwithdrawal plan is likely to be achievable, for example, “There is an85% probability that the pension amount of $5400 per month will beavailable to draw upon in 35 years from now” at the end of theinvestment period, as illustrated in FIG. 4. Or, the user may specifythe probability percentage of an achievable withdrawal plan, and thetarget result optimizing application returns a pension amount that is inline with the probability percentage specified by the user.

In an example embodiment, the output server 220 prepares the output tobe sent to the client input server 210. The target result optimizingapplication then transmits the output from the output server 220 to theclient input server 210. The client input server 210 then processes theoutput for display on the real-time interactive display 200 for viewingby the user.

In an example embodiment, the target result optimizing applicationreceives an invocation from the user, via the real-time interactivedisplay 200, to activate the optimized interactive strategy. The targetresult optimizing application simulates the optimized interactivestrategy over the course of the time period, on the real-timeinteractive display 200 as illustrated in FIGS. 3-10. The simulatedoptimized interactive strategy renders at least one second optimalstrategy along with the optimal strategy, starting at the target resultstart point and ending at the target result end point. Continuing withthe example of an investment plan as the target result, in an exampleembodiment, the target result optimizing application builds aninvestment strategy table starting at the end of the investment period(i.e., zero years left), the last column in the investment strategytable. In an example embodiment, the target result optimizingapplication may calculate the target result value at the target resultend point using various analytical methods, or for example, using aMonte Carlo simulation. This last column is trivial; the asset value atthe end of the investment period is the same as the amount invested (atthe end of the investment period). The optimal financial instrumentpercentage (i.e., the allocation of the financial instruments in thefinancial portfolio) may be left undefined since the investment periodhas come to an end. To calculate the next to last column in theinvestment strategy table (i.e., one year left in the investmentperiod), the target result optimizing application uses a model of marketbehavior to calculate the distribution of the asset value after oneyear. In an example embodiment, the target result optimizing applicationis not limited to only one model of market behavior. The target resultoptimizing application determines the (optimal) financial instrumentpercentage associated with the best distribution function. In an exampleembodiment, the target result optimizing application saves the bestdistribution function in the table cell along with the (optimal)financial instrument percentage. In an example embodiment, after thetarget result optimizing application determines the optimal strategy andthen determines at least one second optimal strategy, the target resultoptimizing application iteratively determines at least one third optimalstrategy for the target result, at the plurality of intervals in thetimeline between the first location in the timeline and the targetresult start point. In other words, this process is repeated for eachcolumn in the investment strategy table moving from right to left (i.e.,“backwards” according to the time period). Thus, for an investmentstrategy table with N years left in the investment period, whencomputing the (optimal) financial instrument percentage for a cell inthe “N years left” column, the target result optimizing application hasalready determined the (optimal) financial instrument percentage for the“N−1 years left” column for all asset values.

Continuing the example of an investment plan as the target result, thetarget result optimizing application determines the optimal financialstrategy for the financial instruments over time (for example, at yearlypoints) during the investment period. The user may run an automatedsimulation of the results of the withdrawal plan over the investmentperiod to allow the user to see a big picture overview of the results ofthe withdrawal plan over time as illustrated in FIGS. 3-10. In anexample embodiment, the user, such as a financial investment advisor,may run the target result optimizing application on behalf of aninvesting client periodically (i.e., yearly, monthly, etc.) to assessand re-assess current financial strategies, for example to determine ifthe financial plan is meeting the investing client's goals. Theautomated simulation simulates those periodic assessments over thecourse of, for example, the investing client's investment period, andmay re-determine the optimal strategy at each point of time. In anexample embodiment, when the target result optimizing applicationsimulates the optimized interactive strategy over the course of the timeperiod, the target result optimizing application determines a pluralityof optimized interactive strategies, and randomly selects one of theplurality of optimized interactive strategies to present to the user onthe real-time interactive display 200. In an example embodiment, thetarget result optimizing application calculates multiple examplewithdrawal plan scenarios (i.e., many of these iterative executions).When the user executes an automated simulation, the target resultoptimizing application randomly selects one of the calculated withdrawalplan scenarios to present to the user. The output of the automatedsimulation is an example of the results of the overall investment withthe withdrawal plan over time, for example, the assets and thewithdrawal plan in the financial portfolio at any given time during theinvestment period, displayed according to probability percentilesassociated with the assets and a distribution of the investment'sresidual amount (e.g., at the end of the investment period, 10%probability that the asset value will be $166 k and the distribution ofthe investment's residual amount will be $1.31K/month, through 90%probability that the asset value will be $1.4 M, and the distribution ofthe investment's residual amount will be $3.34 k/month, with the assetvalues and probability percentiles calculated at increments between 10%and 90%). In an automated simulation, the target result optimizingapplication creates an interactive annuity table that displays to theuser the monthly annuity over the course of the years during which theinvestment drawn. The user may move a cursor over the electronic displayto view individual values (i.e., the monthly annuity) at any given pointalong the timeline

In an example embodiment, the user may choose to create a predictivematrix as illustrated in FIG. 2 using an investment plan as an exampletarget result. The target result optimizing application builds thepredictive matrix by running multiple simulations with ranges of valuesspecified by the user. The predictive matrix is a table that displayshow outputs vary when the user changes two of the inputs to the targetresult optimizing application. Continuing the example of an investmentplan as the target result, the user may vary inputs such as the age atwhich the user receives a withdrawal amount. The user may vary two ofthe inputs to the target result optimizing application. In response, thetarget result optimizing application creates a table that illustrateshow at least one of the computed properties varies when the two inputs(specified by the user) are varied. For example, if the user iscomputing a pension withdrawal plan (i.e., the goal), the user'sretirement age may vary from 55 to 75 years of age, two years at a time,for a total of 10 different ages/steps between 55 and 75. In otherwords, the rows of the predictive matrix are determined by data enteredby the user specifying “Stop year min”, “Stop year max”, and “Stop yearsteps”. The user's annual investment may vary from $1300 to $1500, atincrements/steps of $200, for a total of 10 different amounts invested.In this example embodiment, as illustrated in FIG. 2, the columns of thepredictive matrix are determined by data entered by the user specifying“Annual investment min”, “Annual investment max” and “Annual investmentsteps” (indicating the granularity of the predictive matrix). The targetresult optimizing application creates a 10×10 table showing the expectedpension amount the user could be expected to draw over the 10 years ofretirement. The user may navigate the predictive matrix using anavigation interface where the user can modify one value, and instantlysee how that modification affects other values within the withdrawalplan. The percentile indicates the level of success the user would liketo achieve. In an example embodiment, the percentile may be set by theuser.

In an example embodiment, the target result optimizing applicationprovides the user with at least one interactive control to interact withthe simulated optimized interactive strategy during the simulating, viathe real-time interactive display 200. As noted above, the user maynavigate the predictive matrix using a navigation interface. As anexample of the navigation interface, and continuing the example of aninvestment plan as the target result, interactive sliders allow the userto adjust the annual investment and the stop year, and instantly viewhow those adjustments impact the asset values. As the user manipulatesthe interactive sliders, the target result optimizing applicationrecalculates the stop time asset value (i.e., the value of the assets ifthe user were to stop investing at the age the user selects using the“stop year” slider).

In an example embodiment, when the target result optimizing applicationsimulates the optimized interactive strategy over the course of the timeperiod, the target result optimizing application incorporates datastreamed from a real time online database into the simulated optimizedinteractive strategy and/or a future simulated optimized interactivestrategy. For example, when the user runs an automated simulation for aninvestment strategy (where an investment plan is the target result), thetarget result optimizing application interfaces with the current marketdata. The target result optimizing application may take a snapshot ofthe current market data for use during the execution of the automatedsimulation. Alternatively, the target result optimizing application maytake a snapshot of the market data and use that data for futuresimulation executions. In an example embodiment, if, during therendering of the automated simulation the target result optimizingapplication determines that the target result is not achievable, thetarget result optimizing application iteratively adjusts at least one ofa plurality of inputs associated with the target result, and simulatesthe optimized interactive strategy until achieving the target result. Inother words, if the output of the automated simulations indicates thatthe desired withdrawal plan (i.e., the goal) is not attainable, theoutput server 220 re-runs the automated simulations using the range ofacceptable monthly deposit (i.e., max and min ranges supplied by theuser within the interactive input screen). The output of the automatedsimulations is the withdrawal plan and the probability that thewithdrawal plan will be achieved. In an example embodiment, if, duringthe rendering of the automated simulation, the target result optimizingapplication determines that the target result is achievable, the targetresult optimizing application iteratively adjusts at least one of aplurality of inputs associated with the target result, and simulates theoptimized interactive strategy until the target result is within anacceptable target result range. In other words, if the withdrawal planis achievable, the output server 220 re-runs the automated simulationwith adjusted inputs until the output of the automated simulation iswithin the range of the withdrawal plan. Thus, if the user wereexecuting the target result optimizing application to determine aninvestment plan, the range would provide the user with flexibility interms of, for example, how much the user would need to invest, orperiodic contribution amounts. In an example embodiment, if, during therendering of the automated simulation, the target result optimizingapplication determines that the target result is not achievable, thetarget result optimizing application determines a highest suboptimaltarget result that is achievable and a highest statistical probabilitythat the highest suboptimal target result is achievable. The targetresult optimizing application may automatically modify at least one of aplurality of inputs, and/or may prompt the user to change at least oneof the plurality of inputs. In other words, if the withdrawal plan(i.e., the goal) is not achievable, the target result optimizingapplication will return the highest amount of money that is achievablewith the highest probability. The output server 220 then determines ifthere are any additional simulations to be executed (i.e., was theautomated simulation executed for each year specified within the inputparameters), and if so, re-runs the automated simulations for each ofthose years.

In an example embodiment, during the simulating of the optimizedinteractive strategy, the target result optimizing application mayobtain at least one sub target result from the user, and render thesimulated optimized interactive strategy with the incorporated into theoptimized interactive strategy. The target result optimizing applicationmay also obtain at least one sub target result from the user via thereal-time interactive display 200, where the sub target result occursbetween the target result start point and the target result end point,and then incorporate the sub target result into the optimizedinteractive strategy. The target result optimizing application mayprovide the user with at least one interactive control to incorporate atleast one sub target result into the simulated optimized interactivestrategy. The sub target result may comprise adding a first asset and/orremoving a second asset. In other words, during the execution of theautomated simulation (i.e., the optimized interactive strategy), usingan example embodiment of an automated simulation of an investmentstrategy, the user may stop the automated simulation to insert datapoints where the user wishes to withdraw funds from (or add funds to)the investment portfolio. For example, the user may start investing atage 32, and plan to draw on the investment portfolio at age 70. However,the user may anticipate that, at age 50, the user will have to draw onthe investment portfolio, each year, for five years, to put a childthrough college. Or, the user may receive a bonus that he/she wants toadd to the investment portfolio. The user may add these data points tothe automated simulation, and the target result optimizing applicationwill incorporate these data points into the automated simulation andrisk analysis. In the example where the user withdraws funds from theinvestment portfolio during the investment period, the target resultoptimizing application rebalances the investments such that the userwill be able to withdraw the funds at the points in the timelinespecified by the user, and such that the liquidity of the investmentswill allow the user to withdraw the needed funds during that five yearperiod. In an example embodiment, the target result optimizingapplication then recalculates the withdrawal plan at the end of theinvestment period (for example, when the user draws a pension amountfrom the investment portfolio), and verifies whether the user specifiedwithdrawal plan is still achievable within the probability percentagespecified by the user. In performing this recalculation, the targetresult optimizing application incorporates into the new calculation thewithdrawals that the user planned for within the investment period. Forexample, as noted above, the user may plan to withdraw from thefinancial portfolio midway through the investment period to pay for achild's college education. In this scenario, the target resultoptimizing application recalculates the user's pension (at the end ofthe investment period), and the probability percentage that the userwill be able to withdraw this amount at retirement. If the retirementwithdrawal plan is not within the range specified by the user, thetarget result optimizing application may recommend changes to the user'sinvestment strategy, such as increasing the amount the user periodicallyinvests in the financial portfolio (for example, increasing monthlydeductions from the user's paycheck). In an example embodiment, if theuser runs an automated simulation that does not meet the withdrawalplan, the user may adjust inputs, such as the withdrawal plan, theperiodic deposit, the age at which the user begins to draw from theannuity, etc., and then re-run the automated simulation to perform therisk analysis by creating a probability simulation using the updatedinputs. The user may continue to modify the simulation execution untilthe user is satisfied with the withdrawal plan and the risk profile.

In an example embodiment, when the client input server 210 receives thetarget result from the user via the real-time interactive display 200,the target result optimizing application automatically interfaces withan online account associated with the user to transmit, from the onlineaccount to the client input server 210, input relevant to the targetresult. The automatic interfacing may comprise automatically logginginto the online account. In an example embodiment, the client inputserver 210 server receives input parameters within the interactive inputscreen, for example, from the user. Using the example of an investmentplan as the target result, the target result optimizing application mayinterface with an online database of historical financial data, such asstocks, bonds, inflation, etc., to obtain financial data for the inputparameters. In another example embodiment, the client input server 210interfaces with online financial accounts to obtain financial data(associated with the user) for the input parameters. For example, theclient input server 210 may automatically log into the user's onlinebank accounts to obtain financial data, including account balances,financial portfolios, etc.

In an example embodiment, the client input server 210 receives thetarget result from the user via the real-time interactive display 200,and receives the goal from the user. When the optimizer application 230optimizes the interactive strategy, the optimizer application 230determines the statistical probability (that the target result isachievable) based on the goal inputted by the user. In another exampleembodiment, when the client input server 210 receives the target resultfrom the user via the real-time interactive display 200, the clientinput server 210 may also receive the statistical probability from theuser. In this example embodiment, the optimizer application determinesthe goal based on the statistical probability inputted by the user. Theoptimizer application 230 may also automatically modify at least one ofthe plurality of inputs and/or prompt the user to change at least one ofthe plurality of inputs. The optimizer application 230 then re-optimizesat least a portion of the interactive strategy. Using the example of aninvestment plan as the target result, the plurality of inputs maycomprise an investment period, an annuity drawing period, an initialinvestment, an investment maximum, a target annuity, and/or a periodicdeposit.

In an example embodiment, when the model generator models the futureperformance model associated with the target result, the model generatormodels the future performance model comprising resources used to achievethe target result, and an allocation of each of the resources in thefuture performance model. The target result optimizing applicationdetermines the allocation of each of the resources at any point duringthe time period to attain a highest probability of achieving the targetresult. The target result optimizing application may also provide arecommendation to redistribute the allocation of each of the resourcesto achieve the target result. Continuing with the example of aninvestment plan as the target result, in an example embodiment, thetarget result optimizing application models the future performance offinancial markets, and determines an optimal financial strategy for thefinancial instruments (in the investment portfolio) periodically overtime (for example, at yearly points) during the investment period.Financial strategies include how the financial instruments are allocatedwithin the financial portfolio. The allocation of the financialinstruments is used to determine the projections of the performance ofthe financial portfolio. The model generator may model the futureperformance model using data streamed from a real time online database.The model generator may also model the future performance model using anexample of a past performance.

In an example embodiment, when the model generator models the futureperformance model associated with the target result, the target resultoptimizing application calculates a probability distribution functionfor the target result at the target result end point. The target resultoptimizing application chooses between two or more distributionfunctions when determining the best distribution function. In an exampleembodiment, when the target result optimizing application calculates theprobability distribution function for the target result at the targetresult end point, the target result optimizing application selects thedistribution function target result value from at least two distributionfunctions based on a risk profile specified by the user in the real-timeinteractive display 200. The target result optimizing application makesthis selection depending on the risk profile specified by the user, forexample, in the user input screen. Using the example of an investmentplan as the target result, if all distribution functions reveal that thewithdrawal plan (i.e., the goal) is achievable within the probabilitypercentage (that the withdrawal plan will be achieved), then the targetresult optimizing application may choose the distribution function thatpredicts the highest expected asset value at the end of the investmentperiod (i.e., the time period). The range of “within the probabilitypercentage” means the user specified probability percentage orbetter/higher. If the distribution functions reveal that none of themwill achieve the withdrawal plan within the probability percentage, thenthe target result optimizing application selects the distributionfunction that predicts achieving the highest annuity amount within theuser specified probability percentage. Thus, the withdrawal plan may beadjusted (for example, the annuity amount may be lowered) so that it ispossible to achieve the withdrawal plan with a given probability.

As noted above, and continuing to use the example of an investment planas a target result, the target result optimizing application uses amodel of market behavior to calculate the distribution of the assetvalue after one year (when calculating the next to last column in theinvestment strategy table). The target result optimizing applicationstochastically models the financial markets by modeling, for example,inflation, stock yields, and bond yields. In an example embodiment, thetarget result optimizing application maintains a table of past marketbehavior during a period of years. An example model assumes that eachyear in the future will behave like a randomly selected year from thepast. The target result optimizing application may also select aplurality of consecutive years to model year-to-year correlation. Inanother example embodiment, the target result optimizing applicationuses a covariance matrix that contains the means and the crosscorrelations between inflation, stock yield, and bond yield. This otherexample model assumes that years are independent, and the covariancematrix itself may come from any source, including computing statisticmeasures from a table of historical data. A model may be automaticallyupdated by streaming live market data (for example one or many livefeeds of market data) in building the historical model.

Continuing with the example of an investment plan as the target value,in an example embodiment, during the execution of an automatedsimulation, the target result optimizing application creates aninvestment deposits table that displays the monthly deposits over thecourse of the years of investment. The user may add additionalinvestments at any time during the years of investments (for example, ifthe user receives a bonus and chooses to add the bonus to the investmentportfolio), and the investments table displays these additionalinvestments visually differentiated from the monthly investments. Theuser may move a cursor over the electronic display to view individualvalues (i.e., the monthly deposits and/or additional investments) at anygiven point along the timeline.

In an example embodiment, the user may re-run the automated simulation,modifying the values. As the automated simulation is running, the usermay stop, start, reverse, fast-forward, go back to the beginning and/orgo straight to the end. In FIGS. 5-8, a user may follow the trajectory,step by step year by year etc. Continuing with the example of aninvestment plan as the target value, in an example embodiment, the usermay also start and stop the automated simulation to add an additionalinvestment to the current point in the automated simulation. Forexample, the user may stop the automated simulation at a particularpoint in the timeline to add an additional investment, and then re-startthe automated simulation at the point where it was halted. The targetresult optimizing application continues its execution of simulating theforecast of the investment portfolio, displaying the additionalinvestment along with the periodic investments along the timeline(visually differentiating between the additional investment and theperiodic investments). The target result optimizing applicationincorporates any additional investments into the forecasting of theannuity payments.

Continuing with the example of an investment plan as the target value,in an example embodiment, the user may create and record multipleautomated simulations of various financial strategies to review at alater time. For example, the user may create a financial strategy to setup a particular investment fund. The user may then run automatedsimulations on that particular investment fund to analyze the investmentstrategy. The investment strategy may then be used to improve thecreation of future similar funds. The withdrawal plan may be extended toinclude stochastic events. In other words, the time periods ofwithdrawals and the values of those withdrawals may be specified asprobability distributions instead of fixed amounts. Similarlyinvestments may also be modeled as a stochastic process. With theseextensions, the target result optimizing application may be used toprofile more complex scenarios, for example, investment funds where thetime periods when investors invest their funds and when the investorswithdraw the funds is unknown. In another example embodiment, the numberof users making withdrawals may also be unknown, as in the case of aninvestment fund, or a family investment portfolio where multiple familymembers make withdrawals.

Continuing with the example of an investment plan as the target value,in an example embodiment, during the execution of an automatedsimulation, the target result optimizing application calculates a Profit& Loss table, itemizing the profit and loss of each of the investmentinstruments within the investment portfolio. For example, if theinvestment portfolio is a mix of stocks and bonds, the target resultoptimizing application renders the stock profit, the stock loss, thebond profit, and the bond loss. The target result optimizing applicationpresents this information in both graphical and tabular format. Theelectronic graphical format is interactive, providing additionalinformation as the user moves a cursor over the graph. In an exampleembodiment, the target result optimizing application interfaces withfinancial instruments, for example, the stocks and bonds market, realtime, to obtain this data as frequently as desired (i.e., daily, weekly,monthly, etc.).

A method and system for optimizing a target result have been disclosed.

Although embodiments disclosed herein have been described in accordancewith the embodiments shown, one of ordinary skill in the art willreadily recognize that there could be variations to the embodiments andthose variations would be within the spirit and scope of embodimentsdisclosed herein. Accordingly, many modifications may be made by one ofordinary skill in the art without departing from the spirit and scope ofthe appended claims.

What is claimed is:
 1. A method of optimizing a target result, themethod comprising: transmitting, from a real-time interactive display toa target result optimizing application of a client input server, atarget result from a user, wherein the target result comprises at leastone of a goal, and a statistical probability that the target result isachievable, upon receiving the target result at the client input servervia the real-time interactive display, the target result optimizingapplication automatically interfaces with an online account associatedwith the user to transmit, from the online account to the client inputserver, input relevant to the target result; transmitting, by a clientoutput server to an output server, the target result from the user;compiling, by the output server, an interactive strategy to achieve thetarget result over a timer period beginning at a target result startpoint, and ending at a target result end point, wherein the interactivestrategy comprises a timeline starting at the target result start point,and ending at the target result end point, and wherein the client inputserver transmits the target result to the output server; optimizing, byan optimizer application, at least a portion of the interactive strategyinto an optimal interactive strategy by: i) modeling, by a modelgenerator, at least one future performance model associated with thetarget result, wherein the optimizer application obtains the futureperformance model from the model generator; and ii) determining anoptimal interactive strategy for the target result at the target resultend point, wherein the optimal interactive strategy comprises a targetresult value; transmitting, from the output server to the client inputserver, the optimal interactive strategy; determining at least onesecond optimal interactive strategy for the target result at a firstlocation in the timeline between the target result end point and thetarget result start point, using the future performance model, whereinthe future performance model provides a performance indicator for thetarget result at the first location to achieve a sub target result valueat the target result end point; processing, with the client inputserver, the optimal interactive strategy for display on the real-timeinteractive display; processing, with the client input server, the atleast one second optimal interactive strategy for display on thereal-time interactive display; rendering the optimal interactivestrategy, the statistical probability, and the target result for theuser on the real-time interactive display, and rendering the at leastone second optimal interactive strategy, the statistical probability,and the target result for the user on the real-time interactive display.2. The method of claim 1 further comprising: receiving an invocationfrom the user, via the real-time interactive display, to activate theoptimal interactive strategy; and simulating the optimal interactivestrategy over the course of the time period, on the real-timeinteractive display, wherein the simulated optimal interactive strategyrenders the at least one second optimal interactive strategy and theoptimal interactive strategy, starting at the target result start pointand ending at the target result end point.
 3. The method of claim 2wherein simulating the optimal interactive strategy over the course ofthe time period comprises: determining a plurality of optimalinteractive strategies; and randomly selecting one of the plurality ofoptimal interactive strategies to present to the user on the real-timeinteractive display.
 4. The method of claim 2 wherein simulating theoptimal interactive strategy over the course of the time periodcomprises: incorporating data streamed from a real time online databaseinto at least one of the simulated optimal interactive strategy and afuture simulated optimal interactive strategy.
 5. The method of claim 2wherein simulating the optimal interactive strategy over the course ofthe time period comprises: determining, during the rendering, that thetarget result is not achievable; and iteratively adjusting at least oneof a plurality of inputs associated with the target result andsimulating the optimal interactive strategy until achieving the targetresult.
 6. The method of claim 2 wherein simulating the optimalinteractive strategy over the course of the time period comprises:determining, during the rendering, that the target result is achievable;and iteratively adjusting at least one of a plurality of inputsassociated with the target result and simulating the optimal interactivestrategy until the target result is within an acceptable target resultrange.
 7. The method of claim 2 wherein simulating the optimalinteractive strategy over the course of the time period comprises:determining, during the simulating, that the target result is notachievable; and determining a highest suboptimal target result that isachievable and a highest statistical probability that the highestsuboptimal target result is achievable.
 8. The method of claim 2 furthercomprising: at least one of: i) automatically modifying at least one ofa plurality of inputs; and ii) prompting the user to change the at leastone of the plurality of inputs; and re-simulating the optimalinteractive strategy over the course of the time period.
 9. The methodof claim 2 further comprising: providing the user with at least oneinteractive control to interact with the simulated optimal interactivestrategy during the simulating, via the real-time interactive display.10. The method of claim 9 further comprising: obtaining at least one subtarget result from the user during the simulating the optimalinteractive strategy; and rendering the simulated optimal interactivestrategy with the at least one sub target result incorporated into theoptimal interactive strategy.
 11. The method of claim 9 furthercomprising: obtaining at least one sub target result from the user viathe real-time interactive display, wherein the at least one sub targetresult occurs between the target result start point and the targetresult end point; and incorporating the at least one sub target resultinto the optimal interactive strategy.
 12. The method of claim 11wherein the at least one sub target result comprises at least one of: i)adding a first asset; and ii) removing a second asset.
 13. The method ofclaim 1 wherein receiving, by the client input server, the target resultfrom the user via the real-time interactive display comprises:automatically interfacing with an online account associated with theuser to transmit, from the online account to the client input server,input relevant to the target result, wherein the automaticallyinterfacing comprises automatically logging into the online account. 14.The method of claim 1 wherein receiving, by the client input server, thetarget result from the user via the real-time interactive displaycomprises: receiving the goal from the user; and wherein optimizing, bythe optimizer application, comprises: determining the statisticalprobability based on the goal inputted by the user.
 15. The method ofclaim 1 wherein optimizing, by the optimizer application, comprises: atleast one of: i) automatically modifying at least one of a plurality ofinputs; and ii) prompting the user to change the at least one of theplurality of inputs; and re-optimizing the at least a portion of theinteractive strategy.
 16. The method of claim 15 wherein the pluralityof inputs comprises at least one of: i) an investment period; ii) anannuity drawing period; iii) an initial investment; iv) an investmentmaximum; v) a target annuity; and vi) a periodic deposit.
 17. The methodof claim 1 wherein modeling, by the model generator, the futureperformance model associated with the target result comprises: modelingthe future performance model comprising resources used to achieve thetarget result, and an allocation of each of the resources in the futureperformance model.
 18. The method of claim 17 wherein modeling thefuture performance model comprises resources used to achieve the targetresult, and the allocation of each of the resources in the futureperformance model comprises: determining the allocation of each of theresources at any point during the time period to attain a highestprobability of achieving the target result.
 19. The method of claim 18further comprising: providing a recommendation to redistribute theallocation of each of the resources to achieve the target result. 20.The method of claim 1 wherein modeling, by the model generator, thefuture performance model associated with the target result comprises:modeling the future performance model using data streamed from a realtime online database.
 21. The method of claim 1 wherein modeling, by themodel generator, the future performance model associated with the targetresult comprises: calculating a probability distribution function forthe target result at the target result end point.
 22. The method ofclaim 21 wherein calculating the probability distribution function forthe target result at the target result end point comprises: selectingthe distribution function target result value from at least twodistribution functions based on a risk profile specified by the user inthe real-time interactive display.
 23. The method of claim 1 whereinmodeling, by the model generator, the future performance modelassociated with the target result comprises: modeling the futureperformance model using an example of a past performance.
 24. The methodof claim 1 wherein after determining the optimal interactive strategy,determining the at least one second optimal interactive strategycomprises: iteratively determining at least one third optimalinteractive strategy for the target result, at a plurality of intervalsin the timeline between the first location in the timeline and thetarget result start point, using the future performance model, whereinthe future performance model provides a performance indicator, at eachof the plurality of intervals, to achieve the target result value at thetarget result end point.
 25. The method of claim 24 wherein iterativelydetermining the at least one third optimal interactive strategy for thetarget result, at the plurality of intervals in the timeline between thefirst location in the timeline and the target result start pointcomprises: utilizing a Monte Carlo simulation to calculate the targetresult value at the target result end point.
 26. The method of claim 24wherein iteratively determining the at least one third optimalinteractive strategy for the target result, at the plurality ofintervals in the timeline between the first location in the timeline andthe target result start point comprises: incorporating a previousinterval target result value when determining the at least one thirdoptimal interactive strategy for the target result at a currentinterval, wherein the previous interval target result value is anestimated target result value calculated at a previous interval, whereinthe current interval is closer to the target result start point than theprevious interval.
 27. The method of claim 1 wherein rendering theoptimal interactive strategy comprises: providing the user with arecommendation to achieve the target result.
 28. A computer programproduct for optimizing a target result, the computer program productcomprising: a non-transitory computer readable memory device havingcomputer readable program code embodied therewith, the computer readableprogram code configured to: transmit, from a real-time interactivedisplay to a client input server, a target result from a user, whereinthe target result comprises at least one of a goal, and a statisticalprobability that the target result is achievable, upon receiving thetarget result at the client input server via the real-time interactivedisplay, the target result optimizing application automaticallyinterfaces with an online account associated with the user to transmit,from the online account to the client input server, input relevant tothe target result; transmit, by a client output server to an outputserver, the target result from the user; compile, by the output server,an interactive strategy to achieve the target result over a timer periodbeginning at a target result start point, and ending at a target resultend point, wherein the interactive strategy comprises a timelinestarting at the target result start point, and ending at the targetresult end point, and wherein the client input server transmits thetarget result to the output server; optimize, by an optimizerapplication, at least a portion of the interactive strategy into anoptimal interactive strategy by: i) modeling, by a model generator, atleast one future performance model associated with the target result,wherein the optimizer application obtains the future performance modelfrom the model generator; and ii) determining an optimal interactivestrategy for the target result at the target result end point, whereinthe optimal interactive strategy comprises a target result value; andtransmit, from the output server to the client input server, the optimalinteractive strategy; determine at least one second optimal interactivestrategy for the target result at a first location in the timelinebetween the target result end point and the target result start point,using the future performance model, wherein the future performance modelprovides a performance indicator for the target result at the firstlocation to achieve a sub target result value at the target result endpoint; process, with the client input server, the optimal interactivestrategy for display on the real-time interactive display; process, withthe client input vu the at least one second optimal interactive strategyfor display on the real-time interactive display; render the optimalinteractive strategy, the statistical probability, and the target resultfor the user on the real-time interactive display, and render the atleast one second optimal interactive strategy, the statisticalprobability, and the target result for the user on the real-timeinteractive display.
 29. A system comprising: a processor; and anon-transitory computer readable memory device, having computer readableprogram code embodied therewith, the computer readable program codeconfigured to: transmit, from a real-time interactive display to aclient input server, a target result from a user, wherein the targetresult comprises at least one of a goal, and a statistical probabilitythat the target result is achievable, upon receiving the target resultat the client input server via the real-time interactive display, thetarget result optimizing application automatically interfaces with anonline account associated with the user to transmit, from the onlineaccount to the client input server, input relevant to the target result;transmit, by a client output server to an output server, the targetresult from the user; compile, by the output server, an interactivestrategy to achieve the target result over a timer period beginning at atarget result start point, and ending at a target result end point,wherein the interactive strategy comprises a timeline starting at thetarget result start point, and ending at the target result end point,and wherein the client input server transmits the target result to theoutput server; optimize, by an optimizer application, at least a portionof the interactive strategy into an optimal interactive strategy by: i)modeling, by a model generator, at least one future performance modelassociated with the target result, wherein the optimizer applicationobtains the future performance model from the model generator; ii)determining an optimal strategy for the target result at the targetresult end point, wherein the optimal strategy comprises a target resultvalue; and iii) after determining the optimal strategy, determining atleast one second optimal strategy for the target result at a firstlocation in the timeline between the target result end point and thetarget result start point, using the future performance model, whereinthe future performance model provides a performance indicator for thetarget result at the first location to achieve a sub target result valueat the target result end point; transmit, from the output server to theclient input server, the optimal interactive strategy; process, with theclient input server, the optimal interactive strategy for display on thereal-time interactive display; render the optimal interactive strategy,the statistical probability, and the target result for the user; detectat least one of: i) automatic modification of at least one of aplurality of inputs; and ii) user modification of the at least one ofthe plurality of inputs; and re-optimizing the at least a portion of theinteractive strategy.